Cough detection using frontal accelerometer

ABSTRACT

This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including an accelerometer configured to collect an accelerometer signal that indicates one or more patient movements that occur during a cough. Additionally, the medical device system includes processing circuitry configured to: determine whether the accelerometer signal satisfies a set of criteria corresponding to a cough pattern comprising a smooth increase from a baseline, then a sharp decrease, a peak within the sharp decrease, then a gradual return to the baseline; and identify a cough based on the determination that the accelerometer signal satisfies the set of criteria.

This application is a continuation of U.S. application Ser. No.17/306,372, filed May 3, 2021, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

The disclosure relates generally to medical device systems and, moreparticularly, medical device systems configured to monitor patientparameters.

BACKGROUND

Some types of medical devices may be used to monitor one or morephysiological parameters of a patient. Such medical devices may include,or may be part of a system that includes, sensors that detect signalsassociated with such physiological parameters. Values determined basedon such signals may be used to assist in detecting changes in patientconditions, in evaluating the efficacy of a therapy, or in generallyevaluating patient health.

SUMMARY

In general, the disclosure is directed to devices, systems, andtechniques for using a medical device to detect patient coughs based onan accelerometer signal indicative of one or more movements of thepatient. The accelerometer signal may allow processing circuitry todetermine when the patient coughs, and the character of that cough. Inthis way, the processing circuitry may track a number of instances inwhich the patient coughs over a period of time. If the rate in which thepatient coughs changes over the period of time, or the severity of thecough is extreme, the processing circuitry may determine that thepatient is experiencing one or more patient conditions such as chronicobstructive pulmonary disease (COPD), or experiencing an exacerbation ofone or more patient conditions such as COPD.

For example, a medical device, e.g., an implantable medical device(IMD), may collect one or more accelerometer signals which may includecomponents from various sources including components relating to a coughby the patient. In other words, if a patient coughs, the cough may bereflected in the accelerometer signal collected by the medical device.However, it may be the case that not all components in the accelerometersignal relate to cough(s) by the patient. As such, it may be beneficialto analyze the accelerometer signal to determine whether theaccelerometer signal indicates a cough. For example, processingcircuitry may analyze a section of accelerometer signal. If theaccelerometer signal meets specific criteria, the processing circuitrymay determine that a cough occurred at a time in which the section ofthe accelerometer signal is recorded by the medical device.

In some examples, coughs that may be characterized clinically as “hard”coughs may be of particular interest, e.g., because they indicate thestatus of a condition of the patient, such as COPD, or the health of thepatient in general. In some examples, the specific criteria may beconfigured to identify hard coughs in the accelerometer signal.

In some examples, the accelerometer signal includes a verticalcomponent, a lateral component, and a frontal component corresponding toa vertical axis, a lateral axis, and a frontal axis, respectively. Inthis way, the accelerometer signal represents a three-dimensionalmeasurement of acceleration. It may be beneficial to analyze the frontalcomponent of the accelerometer signal to determine whether theaccelerometer signal indicates a cough. For example, when coughing,especially when coughing hard, a patient may lean slightly forwardsharply, causing the medical device to move along the frontal axis, theforward movement being reflected in the frontal component of theaccelerometer signal. In response to the processing circuitry detectingforward movement in the frontal component of the accelerometer signalfollowing a certain pattern, the processing circuitry may determine thata cough occurred.

The techniques of this disclosure may provide one or more advantages.For example, detecting one or more patient coughs based on only thefrontal accelerometer signal collected by a medical device may allowaccurate detection of coughs while using less computing power and fewerdevices than methods employing an accelerometer signal along withmeasuring other patient parameters. More specifically, it may bebeneficial to detect portions of the accelerometer signal which possiblyindicate a cough, and subsequently analyze portions of the accelerometersignal in order to confirm that the accelerometer signal indicates acough. Additionally, it may be beneficial to analyze the frontalcomponent of the accelerometer system when detecting coughs using theaccelerometer signal since the patient moves their chest forward alongthe frontal axis during a cough.

In some examples, a medical device system is configured to detect one ormore coughs of a patient, the medical device system includes a medicaldevice including an accelerometer configured to collect an accelerometersignal, wherein the accelerometer signal is indicative of one or morepatient movements that occur during a cough. Additionally, the medicaldevice system includes processing circuitry configured to determinewhether the accelerometer signal satisfies a set of criteriacorresponding to a cough pattern comprising a smooth increase from abaseline, then a sharp decrease, a peak within the sharp decrease, thena gradual return to the baseline; and identify a cough based on thedetermination that the accelerometer signal satisfies the set ofcriteria.

In some examples, a method includes: collecting, using an accelerometerof a medical device, an accelerometer signal, wherein the accelerometersignal is indicative of one or more patient movements that occur duringa cough; determining whether the accelerometer signal satisfies a set ofcriteria corresponding to a cough pattern comprising a smooth increasefrom a baseline, then a sharp decrease, a peak within the sharpdecrease, then a gradual return to the baseline; and identifying a coughbased on determining that the accelerometer signal satisfies the set ofcriteria.

In some examples, a non-transitory computer-readable medium includesinstructions for causing one or more processors to determine whether anaccelerometer signal satisfies a set of criteria corresponding to acough pattern comprising a smooth increase from a baseline, then a sharpdecrease, a peak within the sharp decrease, then a gradual return to thebaseline, wherein the accelerometer signal is collected by anaccelerometer of a medical device, and is indicative of one or morepatient movements that occur during a cough; and identify a cough basedon the determination that the accelerometer signal satisfies the set ofcriteria.

The summary is intended to provide an overview of the subject matterdescribed in this disclosure. It is not intended to provide an exclusiveor exhaustive explanation of the systems, device, and methods describedin detail within the accompanying drawings and description below.Further details of one or more examples of this disclosure are set forthin the accompanying drawings and in the description below. Otherfeatures, objects, and advantages will be apparent from the descriptionand drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates the environment of an example medical device systemin conjunction with a patient, in accordance with one or more techniquesof this disclosure.

FIG. 2 is a conceptual drawing illustrating an example configuration ofthe implantable medical device (IMD) of the medical device system ofFIG. 1 , in accordance with one or more techniques described herein.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of the IMD of FIGS. 1 and 2 , in accordance with one ormore techniques described herein.

FIGS. 4A and 4B illustrate two additional example IMDs that may besubstantially similar to the IMD of FIGS. 1-3 , but which may includeone or more additional features, in accordance with one or moretechniques described herein.

FIG. 5 is a block diagram illustrating an example configuration ofcomponents of the external device of FIG. 1 , in accordance with one ormore techniques of this disclosure.

FIG. 6 is a block diagram illustrating an example system that includesan access point, a network, external computing devices, such as aserver, and one or more other computing devices, which may be coupled tothe IMD, the external device, and the processing circuitry of FIG. 1 viaa network, in accordance with one or more techniques described herein.

FIG. 7 is a flow diagram illustrating an example operation foridentifying a cough event from an accelerometer signal, in accordancewith one or more techniques of this disclosure.

FIG. 8 is a graph illustrating an example accelerometer signal during acough, highlighting features indicative of a cough, in accordance withone or more techniques of this disclosure.

FIG. 9 is a graph illustrating an example long-term average signal ofthe accelerometer signal of FIG. 8 , highlighting features indicative ofa cough, in accordance with one or more techniques of this disclosure.

Like reference characters denote like elements throughout thedescription and figures.

DETAILED DESCRIPTION

This disclosure describes techniques for detecting coughs of a patientin order to track one or more patient conditions. Changes in coughfrequency may be a sign of a change in a patient condition. An increasedcoughing frequency or severity detected in a patient may indicate anexasperation in need of one or more medical conditions. For example,coughing frequency may provide an important metric for monitoring one ormore patient conditions such as Chronic Obstructive Pulmonary Disease(COPD). Data collected by an implantable medical device (IMD), or one ormore implantable or external devices, may be used to detect coughs anddetermine coughing frequency. For example, an IMD, or one or more otherdevices, may be configured to record an accelerometer signal which mayinclude information that may be analyzed to detect coughs.

FIG. 1 is a conceptual diagram illustrating an environment of an examplemedical device system 2 in conjunction with a patient 4, in accordancewith one or more techniques of this disclosure. The example techniquesmay be used with an IMD 10, which may be in wireless communication withat least one of external device 12 and other devices not pictured inFIG. 1 . Processing circuitry 14 is conceptually illustrated in FIG. 1as separate from IMID 10 and external device 12 but may be processingcircuitry of IMD 10 and/or processing circuitry of external device 12.In general, the techniques of this disclosure may be performed byprocessing circuitry 14 of one or more devices of a system, such as oneor more devices that include sensors that provide signals, or processingcircuitry of one or more devices that do not include sensors, butnevertheless analyze signals using the techniques described herein. Forexample, another external device (not pictured in FIG. 1 ) may includeat least a portion of processing circuitry 14, the other external deviceconfigured for remote communication with IMD 10 and/or external device12 via a network.

In some examples, IMD 10 is implanted outside of a thoracic cavity ofpatient 4 (e.g., subcutaneously in the pectoral location illustrated inFIG. 1 ). IMD 10 may be positioned near the sternum near or just belowthe level of patient 4's heart, e.g., at least partially within thecardiac silhouette. In some examples, IMD 10 takes the form of a LINQ™Insertable Cardiac Monitor (ICM), available from Medtronic plc, ofDublin, Ireland.

Although in one example IMD 10 takes the form of an ICM, in otherexamples, IMD 10 takes the form of any combination of implantablecardiac devices (ICDs) with intravascular or extravascular leads,pacemakers, cardiac resynchronization therapy devices (CRT-Ds),neuromodulation devices, left ventricular assist devices (LVADs),implantable sensors, cardiac resynchronization therapy pacemakers(CRT-Ps), implantable pulse generators (IPGs), orthopedic devices, ordrug pumps, as examples. Moreover, techniques of this disclosure may beused to measure one or more patient parameters based on signalscollected by one or more of the aforementioned devices. Additionally, oralternatively, techniques of this disclosure may be used to measure oneor more patient parameters based on signals collected by one or moreexternal devices such as patch devices, wearable devices (e.g., smartwatches), wearable sensors, or any combination thereof.

Clinicians sometimes diagnose patients with medical conditions based onone or more observed physiological signals collected by physiologicalsensors, such as electrodes, optical sensors, chemical sensors,temperature sensors, acoustic sensors, and motion sensors. In somecases, clinicians apply non-invasive sensors to patients in order tosense one or more physiological signals while a patent is in a clinicfor a medical appointment. However, in some examples, physiologicalmarkers (e.g., coughs, hard coughs, irregular heartbeats, and long-termrespiration trends) of a patient condition occur when the patient isoutside the clinic. As such, in these examples, a clinician may beunable to observe the physiological markers needed to diagnose a patientwith a medical condition. Additionally, it may be beneficial to monitorone or more patient parameters for an extended period of time (e.g.,days, weeks, or months) so that a frequency of occurrence over time of asymptom may be determined and tracked. In the example illustrated inFIG. 1 , IMD 10 is implanted within patient 4 to continuously record oneor more physiological signals of patient 4 over an extended period oftime.

In some examples, IMD 10 includes one or more accelerometers. Anaccelerometer of IMD 10 may collect an accelerometer signal whichreflects a measurement of a motion of patient 4. In some cases, theaccelerometer may collect a three-axis accelerometer signal indicativeof patient 4's movements within a three-dimensional Cartesian space. Forexample, the accelerometer signal may include a vertical axisaccelerometer signal vector, a lateral axis accelerometer signal vector,and a frontal axis accelerometer signal vector. The vertical axisaccelerometer signal vector may represent an acceleration of patient 4along a vertical axis, the lateral axis accelerometer signal vector mayrepresent an acceleration of patient 4 along a lateral axis, and thefrontal axis accelerometer signal vector may represent an accelerationof patient 4 along a frontal axis. In some cases, the vertical axissubstantially extends along a torso of patient 4 from a neck of patient4 to a waist of patient 4, the lateral axis extends across a chest ofpatient 4 perpendicular to the vertical axis, and the frontal axisextends outward from and through the chest of patient 4, the frontalaxis being perpendicular to the vertical axis and the lateral axis. IMD10 may track accelerometer measurements over a period of time (e.g.,hours, days, weeks, or months) and the processing circuitry may identifya trend of accelerometer values using data from the accelerometermeasurements. Based on the identified trend, the processing circuitrymay, in some cases, identify a medical condition present in the patientor monitor a condition that is already known to be present in thepatient.

External device 12 may be a computing device configured for use insettings such as a home, clinic, or hospital, and may further beconfigured to communicate with IMD 10 via wireless telemetry. Forexample, external device 12 may be coupled to a remote patientmonitoring system, such as Carelink®, available from Medtronic plc, ofDublin, Ireland. External device 12 may, in some examples, include aprogrammer, an external monitor, or a consumer device such as a smartphone or tablet.

In other examples, external device 12 may be a larger workstation or aseparate application within another multi-function device, rather than adedicated computing device. For example, the multi-function device maybe a notebook computer, tablet computer, workstation, one or moreservers, cellular phone, personal digital assistant, or anothercomputing device that may run an application that enables the computingdevice to operate as a secure device.

When external device 12 is configured for use by the clinician, externaldevice 12 may be used to transmit instructions to IMD 10. Exampleinstructions may include requests to set electrode combinations forsensing and any other information that may be useful for programminginto IMD 10. The clinician may also configure and store operationalparameters for IMD 10 within IMD 10 with the aid of external device 12.In some examples, external device 12 assists the clinician in theconfiguration of IMD 10 by providing a system for identifyingpotentially beneficial operational parameter values.

Whether external device 12 is configured for clinician or patient use,external device 12 is configured to communicate with IMD 10 and,optionally, another computing device (not illustrated by FIG. 1 ), viawireless communication. External device 12, for example, may communicatevia near-field communication technologies (e.g., inductive coupling, NFCor other communication technologies operable at ranges less than 10-20cm) and far-field communication technologies (e.g., RF telemetryaccording to the 802.11 or Bluetooth® specification sets, or othercommunication technologies operable at ranges greater than near-fieldcommunication technologies). In some examples, external device 12 isconfigured to communicate with a computer network, such as the MedtronicCareLink® Network developed by Medtronic, plc, of Dublin, Ireland. Forexample, external device 12 may send data, such as data received fromIMD 10, to another external device such as a smartphone, a tablet, or adesktop computer, and the other external device may in turn send thedata to the computer network. In other examples, external device 12 maydirectly communicate with the computer network without an intermediarydevice.

Processing circuitry 14, in some examples, may include one or moreprocessors that are configured to implement functionality and/or processinstructions for execution within IMD 10, external device 12, one ormore other devices, or any combination thereof. For example, processingcircuitry 14 may be capable of processing instructions stored in amemory. Processing circuitry 14 may include, for example,microprocessors, digital signal processors (DSPs), application specificintegrated circuits (ASICs), field-programmable gate arrays (FPGAs), orequivalent discrete or integrated logic circuitry, or a combination ofany of the foregoing devices or circuitry. Accordingly, processingcircuitry 14 may include any suitable structure, whether in hardware,software, firmware, or any combination thereof, to perform the functionsascribed herein to processing circuitry 14. Processing circuitry 14 maycontain signal analysis circuitry which may perform signal processingtechniques to extract information indicating the one or more parametersof an accelerometer signal.

Processing circuitry 14 may represent processing circuitry locatedwithin any combination of IMD 10 and external device 12. In someexamples, processing circuitry 14 may be entirely located within ahousing of IMD 10. In other examples, processing circuitry 14 may beentirely located within a housing of external device 12. In otherexamples, processing circuitry 14 may be located within any combinationof IMD 10, external device 12, and another device or group of devicesthat are not illustrated in FIG. 1 . As such, techniques andcapabilities attributed herein to processing circuitry 14 may beattributed to any combination of IMD 10, external device 12, and otherdevices that are not illustrated in FIG. 1 , e.g., one or more serversor computing devices as illustrated with respect to FIG. 6 .

Processing circuitry 14 may analyze an accelerometer signal to determinewhether a patient coughed, e.g., experienced a cough. Processingcircuitry 14 may determine whether one or more parameter valuesassociated with a segment of an accelerometer signal satisfy one or morecriteria, e.g., is greater than a threshold parameter value. It may bebeneficial for processing circuitry 14 to analyze the frontal componentof the accelerometer signal in order to determine whether the patientcoughed during the period of time in which the segment of theaccelerometer signal is being collected. During a cough, the chest ofpatient 4 may move forwards along the frontal axis. In this way, thechest movement which occurs due to a cough may be recorded in thefrontal component of the accelerometer signal. In some examples, theparameter value of the segment of the accelerometer signal maycorrespond to a magnitude value of the frontal component of theaccelerometer signal. Additionally, or alternatively, in some examples,the parameter value may represent one or more of a derivative of thefrontal component of the segment of the accelerometer signal, an areaunder the frontal component of the accelerometer signal, or anotherparameter corresponding to the frontal component, the verticalcomponent, or the lateral component of the accelerometer signal.

A memory (not illustrated in FIG. 1 ) may be configured to storeinformation within medical device system 2 during operation. The memorymay include a computer-readable storage medium or computer-readablestorage device. In some examples, the memory includes one or both of ashort-term memory or a long-term memory. The memory may include, forexample, random access memories (RAM), dynamic random access memories(DRAM), static random access memories (SRAM), magnetic discs, opticaldiscs, flash memories, or forms of electrically programmable memories(EPROM) or electrically erasable and programmable memories (EEPROM). Insome examples, the memory is used to store program instructions forexecution by processing circuitry 14.

The memory may represent a memory located within any one or both of IMD10 and external device 12. In some examples, the memory may be entirelylocated within a housing of IMD 10. In other examples, the memory may beentirely located within a housing of external device 12. In otherexamples, the memory may be located within any combination of IMD 10,external device 12, and another device or group of devices that are notillustrated in FIG. 1 . As such, techniques and capabilities attributedherein to the memory may be attributed to any combination of IMD 10,external device 12, and other devices that are not illustrated in FIG. 1.

In some examples, one or more sensors (e.g., electrodes, motion sensors(e.g., accelerometers), optical sensors, temperature sensors, or anycombination thereof) of IMD 10 may sense one or more signals thatindicate a parameter or set of parameters of a patient. In someexamples, the signal that indicates the parameter includes a pluralityof parameter values, where each parameter value of the plurality ofparameter values represents a measurement, e.g., periodic measurement,of the parameter at a respective interval of time. The plurality ofparameter values may represent a sequence of parameter values, whereeach parameter value of the sequence of parameter values are collectedby IMD 10 at a start of each time interval of a sequence of timeintervals. For example, IMD 10 may perform a parameter measurement inorder to determine a parameter value of the sequence of parameter valuesaccording to a recurring time interval (e.g., every day, every night,every other day, every twelve hours, every hour, or any other recurringtime interval). In another example, IMD 10 may perform a parametermeasurement in response to a patient notification that measurementshould begin. In another example, IMD 10 may constantly performparameter measurements. In this way, IMD 10 may be configured to track arespective patient parameter more effectively as a patient need not bein a clinic for a parameter to be tracked, since IMD 10 is implantedwithin patient 4 and is configured to perform parameter measurementsaccording to recurring or other time intervals without missing a timeinterval.

Processing circuitry 14 may receive a portion of the accelerometersignal that includes the plurality of parameter values. In this way,processing circuitry 14 may receive at least a portion of the sequenceof parameter values such that processing circuitry 14 can analyze thesignal in order to determine whether a signal segment of anaccelerometer is indicative of a cough, e.g., a hard cough. In someexamples, IMD 10 may continuously collect the accelerometer signaland/or parameter values determined from the accelerometer signal at apredetermined frequency. IMD 10, a server, or another storage device mayinclude a buffer or other memory structure which temporarily orpermanently stores the signal and/or parameter values. Processingcircuitry 14 may maintain a cough database which stores a plurality ofsets of data in logs corresponding to coughs of a patient. In somecases, processing circuitry 14 may remove one or more sets of data fromthe cough database.

Each set of data stored by the cough database may include one or moreportions of signals measured by IMD 10, other implantable devices, otherexternal devices, or any combination thereof. For example, IMD 10 maycollect one or more of the accelerometer signal of an accelerometerattached to IMD 10, an accelerometer signal of an accelerometer attachedto other implantable devices, an accelerometer signal of anaccelerometer attached to an external device, e.g., a wearable patchdevice, or any combination thereof. When IMD 10 collects a signal, IMD10 may collect a sequence of samples corresponding to the respectivesignal, and the sequence of samples may represent the signal itself.Consequently, a “portion” of the signal may represent set of consecutivesamples of the signal. Each set of data stored by the cough database mayinclude a portion of each signal of a set of signals, where eachrespective portion corresponds to a respective window of time. In someexamples, the window of time corresponds to a time in which processingcircuitry 14 has received data indicative of a user cough. In someexamples, the window of time corresponds to a time in which processingcircuitry 14 detects accelerometer signal parameters that satisfycriteria corresponding to a cough.

Processing circuitry may update the cough database when prompted. Forexample, processing circuitry 14 may receive data indicative of a cough,collect a set of data during the time in which the cough is beingexperienced, and add the set of data to the plurality of sets of datastored in the cough database along with other data such as date and timestamps, and increment a cough counter in memory.

Processing circuitry 14 may also update the symptom database on arolling basis. For example, processing circuitry 14 may add a set ofdata to the plurality of sets of data stored in the cough database whenprocessing circuitry 14 detects physiological parameters that correspondto a cough.

Processing circuitry 14 may be configured to identify coughs based ondetected physiological parameter data (e.g. accelerometer signal data).Parameter values corresponding to physiological parameters may be storedin a buffer and be analyzed by an algorithm executed on processingcircuitry 14. When the algorithm determines that detected physiologicalparameters correspond to a cough, processing circuitry 14 may alert aphysician that a cough is being experienced and save the detectedphysiological parameters to the cough database.

Processing circuitry 14 may set one or more time windows based on thetimes or the periods of time in which an accelerometer signal satisfiesa criterion or set of criteria corresponding to a cough pattern. Forexample, processing circuitry 14 may set the time window to begin at afirst time and end at a second time, with the first and second timesbeing identified relate to the time or period of time in which detectedaccelerometer signals are determined by an algorithm to satisfy acriterion or set of criteria corresponding to a cough. In some examples,the first time may represent a time slightly before a first criterioncorresponding to a cough is detected in the accelerometer signal. Insome examples, the first time is a predetermined amount of time beforethe time or period of time in which the first criterion corresponding toa cough is detected in the accelerometer signal. In some examples, thesecond time is a predetermined amount of time after the time or periodof time in which the first criterion corresponding to a cough isdetected in the accelerometer signal, where the second time is after thefirst time. In some examples, the second time may represent a timeslightly after a last criterion corresponding to a cough is detected inthe accelerometer signal, where the second time is after the first time.In any case, the time window may include at least a portion of timefollowing the time in which the first feature of a cough is detected.

In some cases, processing circuitry 14 may save, to the cough databasestored in a memory, a set of data including one or more accelerometersignals corresponding to the time associated with criteria correspondingto a cough detection as described above. The set of data may include aset of signal portions. Each signal portion of the set of signalportions corresponds to a respective signal collected by IMD 10 oranother device and each signal portion of the set of signal portionsincludes data corresponding to the window of time selected by processingcircuitry 14 based on the time or period of time in which signal datasatisfying criteria corresponding to cough have been detected. Forexample, the set of data may include a portion of the accelerometersignal collected by IMD 10 from the first time to the second time, and aportion of the accelerometer signal from the first time to the secondtime as modified by an algorithm.

The accelerometer signal recorded by IMD 10 may include a first sequenceof accelerometer signal samples. For example, IMD 10 may collectaccelerometer signal samples at a predetermined or remotely configurablesampling rate in order to collect the first sequence of accelerometersignal samples. Additionally, processing circuitry 14 may be configuredto generate a second sequence of accelerometer signal samples, where thesecond sequence of accelerometer signal samples represents a derivativeof the first sequence of accelerometer signal samples. In this way,processing circuitry 14 may be configured to calculate the derivative ofthe accelerometer signal. Additionally, processing circuitry 14 may beconfigured to generate a third sequence of accelerometer signal samples,where the third sequence of accelerometer signal samples represents along-term average of the first sequence of accelerometer signal samples.In this way, processing circuitry 14 may be configured to calculate thelong-term average of the accelerometer signal. Additionally, processingcircuitry 14 may be configured to generate a fourth sequence ofaccelerometer signal samples, where the fourth sequence of accelerometersignal samples represents a first difference of the first, second, orthird sequence of accelerometer signal samples. In this way, processingcircuitry 14 may be configured to calculate the first difference of theaccelerometer signal. In some examples, it may be easier for processingcircuitry 14 to detect features of a cough event in the derivative,long-term average, or first-difference signal. Although some examplesbelow specify using either the accelerometer signal, the derivativesignal, the long-term average signal, or the first difference signal,features indicative of a cough may be checked for in any one or more ofthese different signal types. Processing circuitry 14 may be configuredto identify portions of the accelerometer signal which indicatedifferent features of a cough of patient 4.

For example, an accelerometer signal may be sampled at 100 hertz (Hz). Along-term average signal may be generated from the frontal accelerometersignal by, for each sample value, computing the median of a number ofprevious samples (e.g., 30 samples), and compiling all the median valuesinto a long-term average signal. For further example, a first-differencesignal may be generated from the long-term average signal by calculatingthe value difference between each sample and another sample a number ofsamples following each sample (e.g. the difference between eachsuccessive sample value), and compiling all the difference values into afirst-difference signal.

The accelerometer signal recorded by IMD 10 may have one or morefeatures indicative of a cough, e.g. hard cough. In order to detectthese features, an algorithm may be executed on processing circuitry 14that analyses the accelerometer signal. The algorithm may determine thata cough event occurs when all of a number of features are present in theaccelerometer signal. In order to conserve computational power,especially for internal devices with a limited power source, thealgorithm may check for features step-by-step, and upon failing toidentify a feature that must be present in order for the algorithm todetermine that a cough event has occurred, terminate further analysis bythe algorithm of that particular accelerometer signal segment, and startanalysis of the next accelerometer signal segment. The algorithm mayalso terminate further analysis of a particular accelerometer signalsegment if one of a plurality of steps executed in order to identifyfeatures of a cough event fails.

The accelerometer signal may be saved in a buffer to be analyzed. Thebuffer may include a number of signal samples sufficient to detect allthe features required to identify a cough. An algorithm executed onprocessing circuitry 14 may analyze a segment of the accelerometersignal in the buffer where the segment starts on the first sample in thebuffer and ends on a sample a predetermined number of samples after thefirst sample. If the algorithm does not identify a first featurenecessary for classifying the segment of the accelerometer signal asindicative of a cough event, the algorithm may end analysis of thatsignal segment and start analysis of a next accelerometer signalsegment. In some examples, the next accelerometer signal segment may beincremented from the first segment by one sample, or a predeterminednumber of samples.

The one or more features of a cough event that the algorithm executed onprocessing circuitry 14 may attempt to identify in a segment of theaccelerometer signal may include a short period of gradual increase inthe frontal accelerometer signal from a baseline, a subsequent sharpdecrease in the frontal accelerometer signal, a peak in theaccelerometer signal within the sharp decrease, and followed by agradual return in the accelerometer signal to a baseline. The algorithmmay also check the segment of the accelerometer signal for excessivenoise, and determine that the presence of noise above a threshold levelindicates faulty signal readings rather than a hard cough. The algorithmmay attempt to identify these features in an order, and if one of thefeatures is not identified, terminate analysis of that segment of theaccelerometer signal.

Analyzing the features of the segment of the accelerometer signal mayinvolve identifying one or more sub-features or performing one or moresteps of a calculation. For example, processing circuitry 14 executingthe algorithm may generate a long-term average signal from the segmentof the accelerometer signal currently being analyzed. Within thelong-term average signal, processing circuitry 14 may identify adrop-off point, a valley point, and a stabilization point. Failing toidentify one of these features may cause processing circuitry 14 toterminate analysis of the accelerometer signal segment. In addition,processing circuitry 14 may determine if a ratio of a number of samplesbetween the stabilization point and valley point to the sampling rate isboth above a threshold value and below a threshold value. In addition,processing circuitry 14 may calculate an amplitude difference betweenthe dropoff point and the valley point, and an amplitude differencebetween the stabilization point and the valley point. If one of theamplitude differences exceeds an upper threshold or fails to exceed alower threshold, Processing circuitry 14 may terminate analysis of theaccelerometer signal segment. Processing circuitry 14 may identify otherfeatures within the accelerometer signal segment, including noisyfluctuations in a portion of the accelerometer signal segment before thedropoff point. Processing circuitry 14 may also identify a peak pointbetween the dropoff point and the valley point. Further description ofthese features may be found below in FIGS. 7-9 .

Processing circuitry 14 may increment, in response to determining thatall features necessary for classifying a frontal accelerometer signal asindicative of a cough event, a cough count value. In some examples,processing circuitry 14 may attach a time stamp to the detected cough sothat processing circuitry 14 may determine a time in which each detectedcough occurs. In some cases, processing circuitry 14 is configured todetermine, based on the cough count value, a cough rate associated withthe patient, where the cough rate represents a number of coughs detectedper unit time. Processing circuitry 14 may be configured to track thecough rate associated with patient 4 over a period of time. In this way,processing circuitry 14 may identify one or more trends in the coughrate over the period of time. In some cases, if the cough rate increasesfrom a first point in time to a second point in time, processingcircuitry 14 may determine, that a patient condition (e.g., COPD) isoccurring and/or worsening. In some examples, processing circuitry 14 isfurther configured to output an alert indicting the occurrence and/orthe worsening of the patient condition identified by processingcircuitry 14.

In some examples, to identify the one or more trends in the cough rate,processing circuitry 14 may perform a statistical process control (SPC)based on cough rate data over a period of time. For example, cough ratedata may include a set of cough rate values that are collected over theperiod of time. Processing circuitry 14 may determine a baseline coughrate value based on the set of cough rate values. If a cough rate valueof the set of cough rate values is greater than the baseline cough ratevalue by more than a threshold cough rate difference value, processingcircuitry 14 may determine that a worsening of one or more patientconditions (e.g., COPD) occurs at a time in which the cough rate valueis measured.

In some examples, it may be beneficial to detect coughing exacerbationsin order to manage one or more patient conditions, such as COPD. Forexample, it may be beneficial to track a coughing frequency of patient 4over a period of time lasting days or weeks. Processing circuitry 14 maydetect acute exacerbation in coughing, allowing patient 4 to receivetreatment for such a condition. Coughing exacerbations may be caused bya respiratory infection, air pollution, or other triggers of lunginflammation.

FIG. 2 is a conceptual drawing illustrating an example configuration ofIMD 10 of the medical device system 2 of FIG. 1 , in accordance with oneor more techniques described herein. In the example shown in FIG. 2 ,IMD 10 may include a leadless, subcutaneously-implantable monitoringdevice having housing 15, proximal electrode 16A, and distal electrode16B. Housing 15 may further include first major surface 18, second majorsurface 20, proximal end 22, and distal end 24. In some examples, IMD 10may include one or more additional electrodes 16C, 16D positioned on oneor both of major surfaces 18, 20 of IMD 10. Housing 15 encloseselectronic circuitry located inside the IMD 10, and protects thecircuitry contained therein from fluids such as body fluids. In someexamples, electrical feedthroughs provide electrical connection ofelectrodes 16A-16D, and antenna 26, to circuitry within housing 15. Insome examples, electrode 16B may be formed from an uninsulated portionof conductive housing 15.

In the example shown in FIG. 2 , IMD 10 is defined by a length L, awidth W, and thickness or depth D. In this example, IMD 10 is in theform of an elongated rectangular prism in which length L issignificantly greater than width W, and in which width W is greater thandepth D. However, other configurations of IMD 10 are contemplated, suchas those in which the relative proportions of length L, width W, anddepth D vary from those described and shown in FIG. 2 . In someexamples, the geometry of the IMD 10, such as the width W being greaterthan the depth D, may be selected to allow IMD 10 to be inserted underthe skin of the patient using a minimally invasive procedure and toremain in the desired orientation during insertion. In addition, IMD 10may include radial asymmetries (e.g., the rectangular shape) along alongitudinal axis of IMD 10, which may help maintain the device in adesired orientation following implantation.

In some examples, a spacing between proximal electrode 16A and distalelectrode 16B may range from about 30-55 mm, about 35-55 mm, or about40-55 mm, or more generally from about 25-60 mm. Overall, IMD 10 mayhave a length L of about 20-30 mm, about 40-60 mm, or about 45-60 mm. Insome examples, the width W of first major surface 18 may range fromabout 3-10 mm, and may be any single width or range of widths betweenabout 3-10 mm. In some examples, a depth D of IMD 10 may range fromabout 2-9 mm. In other examples, the depth D of IMD 10 may range fromabout 2-5 mm, and may be any single or range of depths from about 2-9mm. In any such examples, IMD 10 is sufficiently compact to be implantedwithin the subcutaneous space of patient 4 in the region of a pectoralmuscle.

IMD 10, according to an example of the present disclosure, may have ageometry and size designed for ease of implant and patient comfort.Examples of IMD 10 described in this disclosure may have a volume of 3cubic centimeters (cm³) or less, 1.5 cm³ or less, or any volumetherebetween. In addition, in the example shown in FIG. 2 , proximal end22 and distal end 24 are rounded to reduce discomfort and irritation tosurrounding tissue once implanted under the skin of patient 4.

In the example shown in FIG. 2 , first major surface 18 of IMD 10 facesoutward towards the skin, when IMD 10 is inserted within patient 4,whereas second major surface 20 faces inward toward musculature ofpatient 4. Thus, first and second major surfaces 18, 20 may face indirections along a sagittal axis of patient 4 (see FIG. 1 ), and thisorientation may be maintained upon implantation due to the dimensions ofIMD 10.

Proximal electrode 16A and distal electrode 16B may be used to sensecardiac EGMs (e.g., cardiac ECGs) when IMD 10 is implantedsubcutaneously in patient 4. In some examples, processing circuitry ofIMD 10 also may determine whether cardiac EGMs of patient 4 areindicative of arrhythmia or other abnormalities (e.g., heart failure,sleep apnea, or COPD), which processing circuitry of IMD 10 may evaluatein determining whether a medical condition of patient 4 has changed. Thecardiac EGMs may be stored in a memory of the IMD 10. In some examples,data derived from the EGMs may be transmitted via integrated antenna 26to another medical device, such as external device 12. In some examples,one or both of electrodes 16A and 16B also may be used by IMD 10 tocollect one or more impedance signals (e.g., a subcutaneous tissueimpedance) during impedance measurements performed by IMD 10. In someexamples, such impedance values detected by IMD 10 may reflect aresistance value associated with a contact between electrodes 16A, 16B,and target tissue of patient 4. Additionally, in some examples,electrodes 16A, 16B may be used by communication circuitry of IMD 10 fortissue conductance communication (TCC) communication with externaldevice 12 or another device.

In the example shown in FIG. 2 , proximal electrode 16A is in closeproximity to proximal end 22, and distal electrode 16B is in closeproximity to distal end 24 of IMD 10. In this example, distal electrode16B is not limited to a flattened, outward facing surface, but mayextend from first major surface 18, around rounded edges 28 or endsurface 30, and onto the second major surface 20 in a three-dimensionalcurved configuration. As illustrated, proximal electrode 16A is locatedon first major surface 18 and is substantially flat and outward facing.However, in other examples not shown here, proximal electrode 16A anddistal electrode 16B both may be configured like proximal electrode 16Ashown in FIG. 2 , or both may be configured like distal electrode 16Bshown in FIG. 2 . In some examples, additional electrodes 16C and 16Dmay be positioned on one or both of first major surface 18 and secondmajor surface 20, such that a total of four electrodes are included onIMD 10. Any of electrodes 16A-16D may be formed of a biocompatibleconductive material. For example, any of electrodes 16A-16D may beformed from any of stainless steel, titanium, platinum, iridium, oralloys thereof. In addition, electrodes of IMD 10 may be coated with amaterial such as titanium nitride or fractal titanium nitride, althoughother suitable materials and coatings for such electrodes may be used.

In the example shown in FIG. 2 , proximal end 22 of IMD 10 includesheader assembly 32 having one or more of proximal electrode 16A,integrated antenna 26, anti-migration projections 34, and suture hole36. Integrated antenna 26 is located on the same major surface (e.g.,first major surface 18) as proximal electrode 16A, and may be anintegral part of header assembly 32. In other examples, integratedantenna 26 may be formed on the major surface opposite from proximalelectrode 16A, or, in still other examples, may be incorporated withinhousing 15 of IMD 10. Antenna 26 may be configured to transmit orreceive electromagnetic signals for communication. For example, antenna26 may be configured to transmit to or receive signals from a programmervia inductive coupling, electromagnetic coupling, tissue conductance,Near Field Communication (NFC), Radio Frequency Identification (RFID),Bluetooth®, Wi-Fi®, or other proprietary or non-proprietary wirelesstelemetry communication schemes. Antenna 26 may be coupled tocommunication circuitry of IMD 10, which may drive antenna 26 totransmit signals to external device 12 and may transmit signals receivedfrom external device 12 to processing circuitry of IMD 10 viacommunication circuitry.

IMD 10 may include several features for retaining IMD 10 in positiononce subcutaneously implanted in patient 4. For example, as shown inFIG. 2 , housing 15 may include anti-migration projections 34 positionedadjacent integrated antenna 26. Anti-migration projections 34 mayinclude a plurality of bumps or protrusions extending away from firstmajor surface 18 and may help prevent longitudinal movement of IMD 10after implantation in patient 4. In other examples, anti-migrationprojections 34 may be located on the opposite major surface as proximalelectrode 16A and/or integrated antenna 26. In addition, in the exampleshown in FIG. 2 header assembly 32 includes suture hole 36, whichprovides another means of securing IMD 10 to the patient to preventmovement following insertion. In the example shown, suture hole 36 islocated adjacent to proximal electrode 16A. In some examples, headerassembly 32 may include a molded header assembly made from a polymericor plastic material, which may be integrated or separable from the mainportion of IMD 10.

Electrodes 16A and 16B may be used to sense cardiac EGMs, as describedabove. Additional electrodes 16C and 16D may be used to sensesubcutaneous tissue impedance, in addition to or instead of electrodes16A, 16B, in some examples. In some examples, processing circuitry ofIMD 10 may determine an impedance value of patient 4 based on signalsreceived from at least two of electrodes 16A-16D. For example,processing circuitry of IMD 10 may generate one of a current or voltagesignal, deliver the signal via a selected two or more of electrodes16A-16D, and measure the resulting other of current or voltage.Processing circuitry of IMD 10 may determine an impedance value based onthe delivered current or voltage and the measured voltage or current.

In the example shown in FIG. 2 , IMD 10 includes light emitter(s) 38 anda proximal light detector 40A and a distal light detector 40B(collectively, “light detectors 40”) positioned on housing 15 of IMD 10.Light detector 40A may be positioned at a distance S from lightemitter(s) 38, and a distal light detector 40B positioned at a distanceS+N from light emitter(s) 38. In other examples, IMD 10 may include onlyone of light detectors 40A, 40B, or may include additional lightemitters and/or additional light detectors. Collectively, lightemitter(s) 38 and light detectors 40A, 40B may include an opticalsensor, which may be used to determine StO₂ or SpO₂ values of patient 4.Although light emitter(s) 38 and light detectors 40A, 40B are describedherein as being positioned on housing 15 of IMD 10, in other examples,one or more of light emitter(s) 38 and light detectors 40A, 40B may bepositioned, on a housing of another type of IMD within patient 4, suchas a transvenous, subcutaneous, or extravascular pacemaker or ICD, orconnected to such a device via a lead.

IMD includes one or more accelerometers (not illustrated in FIG. 2 ).Such accelerometers may be 3D accelerometers configured to generatesignals indicative of one or more types of movement of the patient, suchas gross body movement (e.g., motion) of the patient, patient posture,movements associated with the beating of the heart, coughing, or hardcoughing, rales, or other respiration abnormalities. One or more of theparameters monitored by IMD 10 (e.g., impedance, EGM) may fluctuate inresponse to changes in one or more such types of movement. For example,changes in parameter values sometimes may be attributable to increasedpatient motion (e.g., exercise or other physical motion as compared toimmobility) or to changes in patient posture, and not necessarily tochanges in a medical condition. Thus, in some methods of identifying ortracking a medical condition of patient 4, it may be advantageous toaccount for such fluctuations when determining whether a change in aparameter is indicative of a change in a medical condition.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of IMD 10 of FIGS. 1 and 2 , in accordance with one ormore techniques described herein. As seen in FIG. 3 , IMD 10 includeselectrodes 16A-16D (collectively, “electrodes 16”), antenna 26,processing circuitry 50, sensing circuitry 52, communication circuitry54, storage device 56, switching circuitry 58, sensors 62 includingmotion sensor(s) 42, and power source 64.

Motion sensor(s) 42 may include one or more accelerometers capable oftracking motion in one or more axes. For example, in some examples,motion sensor(s) 42 may include a vertical axis accelerometer, a lateralaxis accelerometer, and a frontal axis accelerometer. In some examples,motion sensor(s) 42 may include a multi-axis accelerometer capable oftracking vertical, lateral, and frontal axis motion. Motion sensor(s) 42may be positioned in any known configuration in or on IMD 10.

Processing circuitry 50 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 50 may include,for example, microprocessors, DSPs, ASICs, FPGAs, equivalent discrete orintegrated logic circuitry, or a combination of any of the foregoingdevices or circuitry. Accordingly, processing circuitry 50 may includeany suitable structure, whether in hardware, software, firmware, or anycombination thereof, to perform the functions ascribed herein to IMD 10.In some examples, processing circuitry 50 may represent at least aportion of processing circuitry 14 of FIG. 1 , but this is not required.In some examples, processing circuitry 50 may be separate fromprocessing circuitry 14 of FIG. 1 .

Sensing circuitry 52 and communication circuitry 54 may be selectivelycoupled to electrodes 16 via switching circuitry 58, which may becontrolled by processing circuitry 50. Sensing circuitry 52 may monitorsignals from electrodes 16 in order to monitor electrical activity ofheart (e.g., to produce an EGM), and/or subcutaneous tissue impedance,the impedance being indicative of at least some aspects of patient 4'scardiac activity and/or respiratory patterns. Sensing circuitry 52 alsomay monitor signals from sensors 62, which may include light detectors40, motion sensor(s) 42, and any additional sensors that may bepositioned on IMID 10. In some examples, sensing circuitry 52 mayinclude one or more filters and amplifiers for filtering and amplifyingsignals received from one or more of electrodes 16 and/or sensor(s) 62.In some examples, sensing circuitry 52 may contain an analog to digitalconverter (ADC) for digitizing sensor signals.

Communication circuitry 54 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as external device 12 or another device or sensor, such asa pressure sensing device. Under the control of processing circuitry 50,communication circuitry 54 may receive downlink telemetry from, as wellas send uplink telemetry to, external device 12 or another device withthe aid of an internal or external antenna, e.g., antenna 26. Inaddition, processing circuitry 50 may communicate with a networkedcomputing device via an external device (e.g., external device 12) and acomputer network, such as the Medtronic CareLink® Network developed byMedtronic, plc, of Dublin, Ireland.

A clinician or other user may retrieve data from IMD 10 using externaldevice 12, or by using another local or networked computing deviceconfigured to communicate with processing circuitry 50 via communicationcircuitry 54. The clinician may also program parameters of IMD 10 usingexternal device 12 or another local or networked computing device.

In some examples, storage device 56 includes computer-readableinstructions that, when executed by processing circuitry 50, cause IMD10 and processing circuitry 50 to perform various functions attributedto IMD 10 and processing circuitry 50 herein. Storage device 56 mayinclude one or both of a short-term memory or a long-term memory. Thememory may include, for example, RAM, DRAM, SRAM, magnetic discs,optical discs, flash memories, or forms of EPROM or EEPROM. In someexamples, the memory is used to store program instructions for executionby processing circuitry 50.

Power source 64 is configured to deliver operating power to thecomponents of IMD 10. Power source 64 may include a battery and a powergeneration circuit to produce the operating power. In some examples, thebattery is rechargeable to allow extended operation. In some examples,recharging is accomplished through proximal inductive interactionbetween an external charger and an inductive charging coil withinexternal device 12. Power source 64 may include any one or more of aplurality of different battery types, such as nickel cadmium batteriesand lithium ion batteries. A non-rechargeable battery may be selected tolast for several years, while a rechargeable battery may be inductivelycharged from an external device, e.g., on a daily or weekly basis.

FIGS. 4A and 4B illustrate two additional example IMDs that may besubstantially similar to IMD 10 of FIGS. 1-3 , but which may include oneor more additional features, in accordance with one or more techniquesdescribed herein. The components of FIGS. 4A and 4B may not necessarilybe drawn to scale, but instead may be enlarged to show detail. FIG. 4Ais a block diagram of a top view of an example configuration of an IMD10A. FIG. 4B is a block diagram of a side view of example IMD 10B, whichmay include an insulative layer as described below.

FIG. 4A is a conceptual drawing illustrating another example IMD 10Athat may be substantially similar to IMD 10 of FIG. 1 . In addition tothe components illustrated in FIGS. 1-3 , the example of IMD 10illustrated in FIG. 4A also may include a body portion 72 and anattachment plate 74. Attachment plate 74 may be configured tomechanically couple header assembly 32 to body portion 72 of IMD 10A.Body portion 72 of IMD 10A may be configured to house one or more of theinternal components of IMD 10 illustrated in FIG. 3 , such as one ormore of processing circuitry 50, sensing circuitry 52, communicationcircuitry 54, storage device 56, switching circuitry 58, internalcomponents of sensors 62, and power source 64. In some examples, bodyportion 72 may be formed of one or more of titanium, ceramic, or anyother suitable biocompatible materials.

FIG. 4B is a conceptual drawing illustrating another example IMD 10Bthat may include components substantially similar to IMD 10 of FIG. 1 .In addition to the components illustrated in FIGS. 1-3 , the example ofIMD 10B illustrated in FIG. 4B also may include a wafer-scale insulativecover 76, which may help insulate electrical signals passing betweenelectrodes 16A-16D and/or light detectors 40A, 40B on housing 15B andprocessing circuitry 50. In some examples, insulative cover 76 may bepositioned over an open housing 15 to form the housing for thecomponents of IMD 10B. One or more components of IMD 10B (e.g., antenna26, light emitter 38, motion sensors 42, processing circuitry 50,sensing circuitry 52, communication circuitry 54, switching circuitry58, and/or power source 64) may be formed on a bottom side of insulativecover 76, such as by using flip-chip technology. Insulative cover 76 maybe flipped onto a housing 15B. When flipped and placed onto housing 15B,the components of IMD 10B formed on the bottom side of insulative cover76 may be positioned in a gap 78 defined by housing 15B.

Insulative cover 76 may be configured so as not to interfere with theoperation of IMD 10B. For example, one or more of electrodes 16A-16D maybe formed or placed above or on top of insulative cover 76, andelectrically connected to switching circuitry 58 through one or morevias (not shown) formed through insulative cover 76. Insulative cover 76may be formed of sapphire (i.e., corundum), glass, parylene, and/or anyother suitable insulating material. Sapphire may be greater than 80%transmissive for wavelengths in the range of about 300 nm to about 4000nm, and may have a relatively flat profile. In the case of variation,different transmissions at different wavelengths may be compensated for,such as by using a ratiometric approach. In some examples, insulativecover 76 may have a thickness of about 300 micrometers to about 600micrometers. Housing 15B may be formed from titanium or any othersuitable material (e.g., a biocompatible material), and may have athickness of about 200 micrometers to about 500 micrometers. Thesematerials and dimensions are examples only, and other materials andother thicknesses are possible for devices of this disclosure.

FIG. 5 is a block diagram illustrating an example configuration ofcomponents of external device 12, in accordance with one or moretechniques of this disclosure. In the example of FIG. 5 , externaldevice 12 includes processing circuitry 80, communication circuitry 82,storage device 84, user interface 86, and power source 88.

Processing circuitry 80 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 80 may include,for example, microprocessors, DSPs, ASICs, FPGAs, equivalent discrete orintegrated logic circuitry, or a combination of any of the foregoingdevices or circuitry. Accordingly, processing circuitry 80 may includeany suitable structure, whether in hardware, software, firmware, or anycombination thereof, to perform the functions ascribed herein toexternal device 12. In some examples, processing circuitry 80 mayrepresent at least a portion of processing circuitry 14 of FIG. 1 , butthis is not required. In some examples, processing circuitry 80 may beseparate from processing circuitry 14 of FIG. 1 .

Communication circuitry 82 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as IMD 10. Under the control of processing circuitry 80,communication circuitry 82 may receive downlink telemetry from, as wellas send uplink telemetry to, IMD 10, or another device.

In some examples, storage device 84 includes computer-readableinstructions that, when executed by processing circuitry 80, causeexternal device 12 and processing circuitry 80 to perform variousfunctions attributed to IMD 10 and processing circuitry 80 herein.Storage device 84 may include one or both of a short-term memory or along-term memory. The memory may include, for example, RAM, DRAM, SRAM,magnetic discs, optical discs, flash memories, or forms of EPROM orEEPROM. In some examples, the memory is used to store programinstructions for execution by processing circuitry 80. Storage device 84may be used by software or applications running on external device 12 totemporarily store information during program execution.

Data exchanged between external device 12 and IMD 10 may includeoperational parameters. External device 12 may transmit data includingcomputer readable instructions which, when implemented by IMD 10, maycontrol IMD 10 to change one or more operational parameters and/orexport collected data. For example, processing circuitry 80 may transmitan instruction to IMD 10 which requests IMD 10 to export collected data(e.g., data corresponding to one or both of a cardiac EGM signal and anaccelerometer signal) to external device 12. In turn, external device 12may receive the collected data from IMD 10 and store the collected datain storage device 84.

A user, such as a clinician or patient 4, may interact with externaldevice 12 through user interface 86. User interface 86 includes adisplay (not shown), such as an LCD or LED display or other type ofscreen, with which processing circuitry 80 may present informationrelated to IMD 10 (e.g., EGM signals obtained from at least oneelectrode or at least one electrode combination, impedance signals,motion signals, cough counts, accelerometer signals including detectedcoughs, an impending symptom warning, or any combination thereof). Inaddition, user interface 86 may include an input mechanism to receiveinput from the user. The input mechanisms may include, for example, anyone or more of buttons, a keypad (e.g., an alphanumeric keypad), aperipheral pointing device, a touch screen, or another input mechanismthat allows the user to navigate through user interfaces presented byprocessing circuitry 80 of external device 12 and provide input. Inother examples, user interface 86 also includes audio circuitry forproviding audible notifications, instructions or other sounds to patient4, receiving voice commands from patient 4, or both. Storage device 84may include instructions for operating user interface 86 and formanaging power source 88.

Power source 88 is configured to deliver operating power to thecomponents of external device 12. Power source 88 may include a batteryand a power generation circuit to produce the operating power. In someexamples, the battery is rechargeable to allow extended operation.Recharging may be accomplished by electrically coupling power source 88to a cradle or plug that is connected to an alternating current (AC)outlet. In addition, recharging may be accomplished through proximalinductive interaction between an external charger and an inductivecharging coil within external device 12. In other examples, traditionalbatteries (e.g., nickel cadmium or lithium ion batteries) may be used.In addition, external device 12 may be directly coupled to analternating current outlet to operate.

FIG. 6 is a block diagram illustrating an example system that includesan access point 90, a network 92, external computing devices, such as aserver 94, and one or more other computing devices 100A-100N, which maybe coupled to IMD 10, external device 12, and processing circuitry 14via network 92, in accordance with one or more techniques describedherein. In this example, IMD 10 may use communication circuitry 54 tocommunicate with external device 12 via a first wireless connection, andto communication with an access point 90 via a second wirelessconnection. In the example of FIG. 6 , access point 90, external device12, server 94, and computing devices 100A-100N are interconnected andmay communicate with each other through network 92.

Access point 90 may include a device that connects to network 92 via anyof a variety of connections, such as telephone dial-up, digitalsubscriber line (DSL), or cable modem connections. In other examples,access point 90 may be coupled to network 92 through different forms ofconnections, including wired or wireless connections. In some examples,access point 90 may be a user device, such as a tablet or smartphone,that may be co-located with the patient. As discussed above, IMD 10 maybe configured to transmit data, such as any one or combination of anaccelerometer signal, and a cough count to external device 12. Inaddition, access point 90 may interrogate IMD 10, such as periodicallyor in response to a command from the patient or network 92, in order toretrieve parameter values determined by processing circuitry 50 of IMD10, or other operational or patient data from IMD 10. Access point 90may then communicate the retrieved data to server 94 via network 92.

In some cases, server 94 may be configured to provide a secure storagesite for data that has been collected from IMD 10, and/or externaldevice 12. In some cases, server 94 may assemble data in web pages orother documents for viewing by trained professionals, such asclinicians, via computing devices 100A-100N. One or more aspects of theillustrated system of FIG. 6 may be implemented with general networktechnology and functionality, which may be similar to that provided bythe Medtronic CareLink® Network developed by Medtronic plc, of Dublin,Ireland.

Server 94 may include processing circuitry 96. Processing circuitry 96may include fixed function circuitry and/or programmable processingcircuitry. Processing circuitry 96 may include any one or more of amicroprocessor, a controller, a DSP, an ASIC, an FPGA, or equivalentdiscrete or analog logic circuitry. In some examples, processingcircuitry 96 may include multiple components, such as any combination ofone or more microprocessors, one or more controllers, one or more DSPs,one or more ASICs, or one or more FPGAs, as well as other discrete orintegrated logic circuitry. The functions attributed to processingcircuitry 96 herein may be embodied as software, firmware, hardware orany combination thereof. In some examples, processing circuitry 96 mayperform one or more techniques described herein based on one or moresets of accelerometer data received from IMD 10, or may otherwiserepresent at least a portion of processing circuitry 14 of FIG. 1 .

Server 94 may include memory 98. Memory 98 includes computer-readableinstructions that, when executed by processing circuitry 96, cause IMD10 and processing circuitry 96 to perform various functions attributedto IMD 10 and processing circuitry 96 herein. Memory 98 may include anyvolatile, non-volatile, magnetic, optical, or electrical media, such asRAM, ROM, NVRAM, EEPROM, flash memory, or any other digital media.

In some examples, one or more of computing devices 100A-100N (e.g.,device 100A) may be a tablet or other smart device located with aclinician or physician, by which the clinician may program, receivealerts from, and/or interrogate IMD 10. For example, the clinician mayaccess data corresponding to any one or more of an accelerometer signal,and a cough count collected by IMD 10 through device 100A, such as whenpatient 4 is in between clinician visits, to check on a status of amedical condition. In some examples, the clinician may enterinstructions for a medical intervention for patient 4 into an app indevice 100A, such as based on a status of a patient condition determinedby IMD 10, external device 12, processing circuitry 14, or anycombination thereof, or based on other patient data known to theclinician. Device 100A then may transmit the instructions for medicalintervention to another of computing devices 100A-100N (e.g., device100B) located with patient 4 or a caregiver of patient 4. For example,such instructions for medical intervention may include an instruction tochange a drug dosage, timing, or selection, to schedule a visit with theclinician, or to seek medical attention. In further examples, device100B may generate an alert to patient 4 based on a status of a medicalcondition of patient 4 determined by IMD 10, external device 12,processing circuitry 14, or any combination thereof, which may enablepatient 4 proactively to seek medical attention prior to receivinginstructions for a medical intervention. In this manner, patient 4 maybe empowered to take action, as needed, to address his or her medicalstatus, which may help improve clinical outcomes for patient 4.

FIG. 7 is a flow diagram illustrating an example operation foridentifying a cough event from an accelerometer signal, in accordancewith one or more techniques of this disclosure.

An accelerometer signal segment recorded by IMD 10 may have one or morefeatures indicative of a cough, e.g., hard cough (or coughs). Analgorithm executed on processing circuitry 14 may determine that a coughevent occurs when all of a number of features are present in theaccelerometer signal. In order to conserve computational power,especially for internal devices with a limited power source, processingcircuitry 14 may check for features step-by-step, and upon failing toidentify a feature that must be present in order for processingcircuitry 14 to determine that a cough event has occurred, terminatefurther analysis of that particular accelerometer signal segment.Processing circuitry 14 may also terminate further analysis of aparticular accelerometer signal segment if one of a plurality of stepsexecuted in order to identify features of a cough event fails.

As illustrated by the example of FIG. 7 , processing circuitry 14 maycollect an accelerometer signal set in a buffer (702). The accelerometersignal may come from a frontal accelerometer integrated with IMD 10. Thebuffer may include a number of signal samples sufficient to detect allthe features required to identify a cough.

An algorithm executed on processing circuitry 14 may analyze the nextavailable segment of accelerometer signal samples in the buffer (704).For every iteration of the algorithm, it may advance the buffer to anext segment of accelerometer signal samples. The next segment ofaccelerometer signal samples may be offset from the prior segment by oneor more samples, such that each next segment consists of one or moresignal samples collected in a period of time after the one or moresignal samples from the prior segment. In order to analyze the nextsegment of accelerometer signals, the algorithm may check the signalsegment for a number of features. The algorithm may check for featuresin any order, and may move on to analyzing another signal segment ifunable to identify a feature in the segment currently being analyzed. Inorder to identify these features, processing circuitry 14 may go througha number of other calculation and identification steps which may beperformed in any order. The failure to calculate or identify in theseother steps may also result in the algorithm moving on to analyzinganother signal segment.

Processing circuitry 14 may identify a smooth increase in theaccelerometer signal from a baseline (706). Although in FIG. 7 thisfeature is checked for first, the algorithm may check for other featuresfirst, and may not check for this feature at all if other featurescannot be identified. In order to identify the smooth increase in theaccelerometer signal from the baseline, processing circuitry 14 mayperform one or more steps. Processing circuitry 14 may calculate theamplitude difference between each sample and one or more later samplesin the sample segment being analyzed. For example, processing circuitry14 may calculate the difference between each sample and the 5^(th)following sample, as well as each sample and the 10^(th) followingsample. If the amplitude differences exceed certain values, processingcircuitry 14 may determine that the segment of the accelerometer signalcontaining those samples is indicative of one or more patient upper bodymovements. The segment indicative of one or more patient upper bodymovements may include other samples before the samples for whichamplitude differences were calculated, and other samples afterwards. Ifprocessing circuitry 14 does not identify a sample with amplitudedifferences between it and later samples exceeding threshold values,processing circuitry 14 may stop analyzing the accelerometer signalsegment and move on to another accelerometer signal segment.

In order to identify the smooth increase in the accelerometer signalfrom the baseline, processing circuitry 14 may perform furthercalculations on the sample with amplitude differences between it andlater samples exceeding threshold values (sample one). Processingcircuitry 14 may define a first sample window in the long-term averagesignal a number of samples before and a number of samples after sampleone. For example, processing circuitry 14 may define a first samplewindow spanning 20 samples before sample one and 10 samples after sampleone. Within the first sample window, processing circuitry 14 maycalculate the amplitude difference between each successive sample. Thefirst negative difference calculated in the first sample window isdetermined to be a dropoff point in the long-term average signal and theaccelerometer signal segment. Processing circuitry 14 may define asecond sample window in the accelerometer signal segment including anumber of samples before the dropoff point. For example, processingcircuitry 14 may define the second sample window from 150 samples beforethe dropoff point to 50 samples before the dropoff point. Within thesecond sample window, processing circuitry 14 may identify a maximum andminimum amplitude value, and calculate the difference therebetween. Ifthe difference between the maximum and minimum amplitude values in thesecond sample window exceeds a threshold value, processing circuitry 14may determine that the increase in the accelerometer signal is notsmooth, stop analyzing the accelerometer signal segment, and move on toanother accelerometer signal segment. For example, if processingcircuitry 14 calculates the difference between the maximum and minimumamplitude values in the second sample window to exceed 0.25, thenprocessing circuitry 14 may determine that the increase in theaccelerometer signal is not smooth. If processing circuitry 14determines that the difference between the maximum and minimum amplitudevalues in the second sample window does not exceed a threshold value,processing circuitry 14 may determine that there is a smooth increase inthe accelerometer signal segment from the baseline.

Processing circuitry 14 may identify a sharp decrease in theaccelerometer signal segment (708). Although in FIG. 7 this feature ischecked for second, the algorithm may check for other features second,and may not check for this feature at all if other features cannot beidentified. In order to identify the sharp decrease in the accelerometersignal, processing circuitry 14 may define a valley point in thelong-term average signal. Processing circuitry 14 may define a thirdsample window consisting of a predetermined number of samplesimmediately following the dropoff point. For example processingcircuitry 14 may define the third sample window as the 350 samplesimmediately following the dropoff point. Processing circuitry 14 maythen calculate a second first-difference signal from the third samplewindow. For each point in the second first-difference signal, processingcircuitry 14 may define an earlier window and a later window, consistingof a predetermined number of samples before and after the pointrespectively. For example, processing circuitry 14 may define an earlierwindow of 15 samples before each point and a later window of 15 samplesafter each point in the second first-difference signal. Processingcircuitry 14 may analyze each earlier and later window to determine ifthe values in those windows are positive or negative, as the values ofthe second first-difference signal will be representative of a slope ofthe long-term average signal. If a predetermined majority of samples inthe earlier window are negative, and a predetermined majority of samplesin the later window are positive, processing circuitry 14 may define thesample point around which those windows sit as the valley point. Forexample, if 14 out of 15 sample points in the earlier window arenegative, and 14 out of 15 sample points in the later window arepositive, processing circuitry 14 may define the point around whichthose two windows sit as the valley point. If processing circuitry 14does not identify any point in the second first difference signal wherethe predetermined number of earlier samples are negative and thepredetermined number of later samples are positive, processing circuitry14 may stop analyzing the accelerometer signal segment, and move on toanother accelerometer signal segment.

After identifying a valley point, processing circuitry 14 may determineif the amplitude difference between the dropoff point and the valleypoint in the long-term average signal is within a first range. Forexample, processing circuitry 14 may determine if the amplitudedifference between the dropoff point and the valley point is greaterthan 0.2 and less than 0.6. If the amplitude difference between thedropoff point and the valley point is within the first range, processingcircuitry 14 may identify the sample window between the dropoff pointand the valley point as the sharp decrease in the accelerometer signal.

Processing circuitry 14 may identify a gradual return to a baseline inthe accelerometer signal (710). Although in FIG. 7 this feature ischecked for third, the algorithm may check for other features third, andmay not check for this feature at all if other features cannot beidentified. In order to identify the gradual return of the accelerometersignal to the baseline, processing circuitry 14 may define astabilization point. The stabilization point may be identified by firstdefining a fourth sample window within the second first-differencesignal. The fourth sample window may consist of a number of samplesbetween the valley point and the end of the second first-differencesignal. For example, processing circuitry 14 may define the fourthsample window as starting 15 samples after the valley point and endingat the end of the second first-difference window. Within the fourthsample window, processing circuitry 14 identifies the first negativevalue point. Then a fifth sample window may be defined a predeterminednumber of samples after the first negative value point. For example, thefifth sample window may be defined as 25 samples after the firstnegative value point. Within the fifth sample window, processingcircuitry 14 may determine how many point values are negative and howmany are positive. If there are too many positive values and/or too fewnegative values, processing circuitry 14 may determine that the firstnegative value point does not correspond to a stabilization point, andperform the operation described in this paragraph on the second negativevalue point in the fourth sample window. For example, if there are 1 orfewer negative point values in the fifth sample window, and/or 4 or morepositive point values in the fifth sample window, processing circuitry14 may determine that the first negative value does not correspond to astabilization point, and perform the operation described in thisparagraph on the second negative value point in the fourth samplewindow. If there are few enough positive values and enough negativevalues in the fifth sample window following a negative value point,processing circuitry 14 may determine that the negative value point issatisfactory.

If processing circuitry 14 does not identify any negative value pointsin the fourth sample window, processing circuitry may determine if thereare a predetermined number of consecutive zero values in the fourthsample window. If there are a predetermined number of consecutive zerovalues in the fourth sample window, processing circuitry 14 may definethe first of the consecutive zero values as the stabilization point. Forexample, if processing circuitry 14 determines there are 20 consecutivezero values in the fourth sample window, processing circuitry 14 maydefine the first of the 20 consecutive zero values as the stabilizationpoint. If there are no satisfactory negative value points, and nopredetermined number of consecutive zero values in the fourth samplewindow, processing circuitry 14 may stop analyzing the accelerometersignal segment and move on to another accelerometer signal segment.

If processing circuitry 14 does identify a satisfactory negative valuepoint, processing circuitry may define the stabilization point as thesatisfactory negative value point, but first, processing circuitry 14performs another check. Processing circuitry 14 may define a sixthsample window in the second first-difference signal. The sixth samplewindow be defined from a range of samples between the valley point andthe satisfactory negative value point. For example, processing circuitry14 may define the sixth sample window to be the range of samples betweenthe valley point and 25 samples before the satisfactory negative valuepoint. Within the sixth sample window, processing circuitry 14determines if there are a predetermined number of consecutive zerovalues. If the predetermined number of consecutive zero values ispresent in the sixth sample window, processing circuitry 14 may definethe first zero value point as the stabilization point. If thepredetermined number of consecutive zero values is not present in thesixth sample window, processing circuitry 14 may define the satisfactorynegative value point as the stabilization point. For example, processingcircuitry 14 may check the sixth sample window for 25 consecutive zerovalues. If there are 25 consecutive zero values, processing circuitry 14may define the first zero value as the stabilization point. If there arenot 25 consecutive zero values, processing circuitry 14 may define thesatisfactory negative value point as the stabilization point.

Processing circuitry 14 may determine if there is a gradual return to abaseline in the accelerometer signal with reference to the stabilizationpoint. Processing circuitry 14 may determine if the amplitude differencebetween the stabilization point and the valley point is smaller than athreshold value, and if a ratio of the number of samples between thestabilization point and valley point to the sampling rate is within asecond range. For example, processing circuitry 14 may determine if theamplitude difference between the stabilization point and the valleypoint is less than 0.4, and if the number of samples between thestabilization point and valley point is between 80 and 220 when thesampling rate is 100 Hz. If the amplitude difference between thestabilization point and the valley point is smaller than a thresholdvalue, and if a ratio of the number of samples between the stabilizationpoint and valley point to the sampling rate is within a second range,processing circuitry 14 may identify the sample window between thevalley point and the stabilization point as a slow return to a baselinein the accelerometer signal.

Processing circuitry 14 may identify a peak within the sharp decrease inthe accelerometer signal (712). Although in FIG. 7 this feature ischecked for fourth, the algorithm may check for other features fourth,and may not check for this feature at all if other features cannot beidentified. The algorithm may check for this feature any time afteridentifying a sharp decrease in the accelerometer signal. In order toidentify the peak within the sharp decrease in the accelerometer signal,processing circuitry 14 may define a seventh sample window in theaccelerometer signal. The seventh sample window may be defined as thesamples between the dropoff point and the valley point in theaccelerometer signal. Processing circuitry 14 may then create adifference signal from the seventh sample window by computing theamplitude difference with every fourth sample in the seventh samplewindow. If the difference signal contains a positive threshold crossingand a negative threshold crossing, processing circuitry 14 may determinethat there is a peak within the seventh sample window, and thus withinthe sharp decline. For example, if the difference signal has at leastone positive threshold crossing with a threshold of 0.08 and at leastone negative threshold crossing with a threshold of −0.08, thenprocessing circuitry 14 may determine that there is a peak within theseventh sample window. If processing circuitry 14 does not detectsufficient positive and negative threshold crossings in the differencesignal of the seventh sample window, then processing circuitry 14 maystop analyzing the accelerometer signal segment and move on to anotheraccelerometer signal segment.

Processing circuitry 14 may check to see if the accelerometer signalsegment has features that satisfy all the criteria corresponding to acough (714). If one or more features were not identified, or one or morecriteria not met, processing circuitry 14 may stop analyzing theaccelerometer signal segment and move on to another accelerometer signalsegment (704). If processing circuitry 14 did identify all relevantfeatures satisfying all criteria, processing circuitry 14 may incrementa cough count value, save a set of data to a database in memory (716),and notify a physician of the data set.

Processing circuitry 14 may also check for features indicative of baddata. For example, processing circuitry 14 may check for excessive noisein the accelerometer signal segment. If excessive noise is detected, thealgorithm may determine that the accelerometer signal segment isindicative of a bad data rather than a cough. The algorithm may checkfor excessive noise at any time, and may not check for excessive noiseif other features indicative of a cough, cannot be identified.Processing circuitry 14 may determine excessive noise is present in theaccelerometer signal in a number of ways. In some examples, processingcircuitry 14 may determine that an accelerometer signal segment is toonoisy if a number of accelerometer sample values exceed a firstthreshold, wherein the sample values fluctuate in value a number oftimes above a second threshold, and the excessive number of excessivelyfluctuating accelerometer sample values occur in the accelerometersignal during an amount of time less than a third threshold amount.

FIG. 8 is a graph illustrating an example accelerometer signal during acough, highlighting features indicative of a cough, in accordance withone or more techniques of this disclosure.

Accelerometer signal 800 may contain features indicative of a coughe.g., hard cough (or coughs), such as a smooth increase 802 in theaccelerometer signal from the baseline, a sharp decrease 804 in theaccelerometer signal, a peak 806 within the sharp decrease, and agradual return 808 of the accelerometer signal to the baseline. Thealgorithm may check for features in any order, and may move on toanalyzing another signal segment if unable to identify a feature in thesegment currently being analyzed. The process for identifying thesefeatures is described in more detail above with reference to FIG. 7 .

FIG. 9 is a graph illustrating an example long-term average signal ofthe accelerometer signal of FIG. 8 , highlighting features indicative ofa cough, in accordance with one or more techniques of this disclosure.

In some examples, it may be easier for processing circuitry 14 to detectfeatures of a cough event in the long-term average signal 900.Processing circuitry 14 may define points within the long-term averagesignal, including a dropoff point 902, a valley point 904, and astabilization point 906.

Processing circuitry 14 may determine if the features identified in thelong-term average signal satisfy criteria corresponding to a cough.Processing circuitry 14 may determine if the amplitude differencebetween the dropoff point and the valley point is within a first range,if the amplitude difference between the stabilization point and thevalley point is smaller than a threshold value, and if a ratio of thenumber of samples between the stabilization point and valley point tothe sampling rate is within a second range. For example, processingcircuitry 14 may determine if the amplitude difference between thedropoff point and the valley point is greater than 0.2 and less than0.6, if the amplitude difference between the stabilization point and thevalley point is less than 0.4, and if the number of samples between thestabilization point and valley point is between 80 and 220 when thesampling rate is 100 Hz. The process for identifying these features andchecking for these criteria is described in more detail above withreference to FIG. 7 .

The techniques described in this disclosure may be implemented, at leastin part, in hardware, software, firmware, or any combination thereof.For example, various aspects of the techniques may be implemented withinone or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalentintegrated or discrete logic QRS circuitry, as well as any combinationsof such components, embodied in external devices, such as physician orpatient programmers, stimulators, or other devices. The terms“processor” and “processing circuitry” may generally refer to any of theforegoing logic circuitry, alone or in combination with other logiccircuitry, or any other equivalent circuitry, and alone or incombination with other digital or analog circuitry.

For aspects implemented in software, at least some of the functionalityascribed to the systems and devices described in this disclosure may beembodied as instructions on a computer-readable storage medium such asRAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or formsof EPROM or EEPROM. The instructions may be executed to support one ormore aspects of the functionality described in this disclosure.

In addition, in some aspects, the functionality described herein may beprovided within dedicated hardware and/or software modules. Depiction ofdifferent features as modules or units is intended to highlightdifferent functional aspects and does not necessarily imply that suchmodules or units must be realized by separate hardware or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware or software components, orintegrated within common or separate hardware or software components.Also, the techniques could be fully implemented in one or more circuitsor logic elements. The techniques of this disclosure may be implementedin a wide variety of devices or apparatuses, including an IMD, anexternal programmer, a combination of an IMD and external programmer, anintegrated circuit (IC) or a set of ICs, and/or discrete electricalcircuitry, residing in an IMD and/or external programmer.

What is claimed is:
 1. A medical device system comprising: a medicaldevice comprising: an accelerometer configured to collect anaccelerometer signal, wherein the accelerometer signal is indicative ofone or more patient movements that occur during a cough; and processingcircuitry configured to: determine whether the accelerometer signalsatisfies a set of criteria corresponding to a cough pattern comprisinga smooth increase from a baseline, then a sharp decrease, a peak withinthe sharp decrease, then a gradual return to the baseline; and identifya cough based on the determination that the accelerometer signalsatisfies the set of criteria.
 2. The medical device system of claim 1,wherein the processing circuitry is further configured to increment acough count value.
 3. The medical device system of claim 1, wherein theprocessing circuitry is further configured to: save, to a database inmemory, a set of data including the accelerometer signal; and notify aphysician of the data set.
 4. The medical device system of claim 1,wherein to determine whether the accelerometer signal satisfies the setof criteria corresponding to a cough pattern, the processing circuitryis further configured to: execute an algorithm in a series of stepsdetermining whether each criterion of the set of criteria is satisfiedindividually; and terminate the algorithm without proceeding to thelater steps responsive to one of the criteria not being satisfied. 5.The medical device system of claim 4, wherein to perform the series ofsteps, the processing circuitry is further configured to: determine along-term average signal from the accelerometer signal; identify adropoff point in the long-term average signal; identify a valley pointin the long-term average signal; and identify a stabilization point inthe long-term average signal.
 6. The medical device system of claim 5,wherein to perform the series of steps, the processing circuitry isfurther configured to: determine whether an amplitude difference betweenthe dropoff point and the valley point is greater than a thresholdvalue.
 7. The medical device system of claim 5, wherein to perform theseries of steps, the processing circuitry is further configured to:determine whether an amplitude difference between the stabilizationpoint and the valley point is less than a threshold value.
 8. Themedical device system of claim 5, wherein to perform the series ofsteps, the processing circuitry is further configured to: determinewhether a ratio of a number of samples between the stabilization pointand valley point to the sampling rate is both above a first thresholdvalue and below a second threshold value;
 9. The medical device systemof claim 5, wherein to perform the series of steps, the processingcircuitry is further configured to: determine if noisy fluctuationsexist in the accelerometer signal preceding the dropoff point in thelong-term average signal; and
 10. The medical device system of claim 5,wherein to perform the series of steps, the processing circuitry isfurther configured to: determine if a peak point exists between thedropoff point and the valley point such that a difference signal betweenthe dropoff point and the valley point contains a value above a firstthreshold value and a value below a second threshold value.
 11. A methodcomprising: collecting, using an accelerometer of a medical device, anaccelerometer signal, wherein the accelerometer signal is indicative ofone or more patient movements that occur during a cough; determiningwhether the accelerometer signal satisfies a set of criteriacorresponding to a cough pattern comprising a smooth increase from abaseline, then a sharp decrease, a peak within the sharp decrease, thena gradual return to the baseline; and identifying a cough based ondetermining that the accelerometer signal satisfies the set of criteria.12. The method of claim 11, wherein the method further comprisesincrementing a cough count value in response to identifying a cough. 13.The method of claim 11, wherein the method further comprises: saving, toa database in memory, a set of data including the accelerometer signal;and notifying a physician of the data set.
 14. The method of claim 11,wherein determining whether the accelerometer signal satisfies the setof criteria corresponding to a cough pattern further comprises:executing an algorithm in a series of steps determining whether eachcriterion of the set of criteria is satisfied individually; andterminating the algorithm without proceeding to the later stepsresponsive to one of the criteria not being satisfied.
 15. The method ofclaim 14, wherein the series of steps comprises: determining a long-termaverage signal from the accelerometer signal; identifying a dropoffpoint in the long-term average signal; identifying a valley point in thelong-term average signal; and identifying a stabilization point in thelong-term average signal.
 16. The method of claim 15, wherein the seriesof steps further comprises: determining whether the amplitude differencebetween the dropoff point and the valley point is greater than athreshold value;
 17. The method of claim 15, wherein the series of stepsfurther comprises determining whether the amplitude difference betweenthe stabilization point and the valley point is less than a thresholdvalue.
 18. The method of claim 15, wherein the series of steps furthercomprises determining whether a ratio of a number of samples between thestabilization point and valley point to the sampling rate is both abovea first threshold value and below a second threshold value;
 19. Themethod of claim 15, wherein the series of steps further comprises:determining if noisy fluctuations exist in the accelerometer signalpreceding the dropoff point in the long-term average signal; anddetermining if a peak point exists between the dropoff point and thevalley point such that a difference signal between the dropoff point andthe valley point contains a value above a first threshold value and avalue below a second threshold value.
 20. A non-transitorycomputer-readable medium comprising instructions for causing processingcircuitry of a medical device system to: determine whether anaccelerometer signal satisfies a set of criteria corresponding to acough pattern comprising a smooth increase from a baseline, then a sharpdecrease, a peak within the sharp decrease, then a gradual return to thebaseline, wherein the accelerometer signal is collected by anaccelerometer of a medical device, and is indicative of one or morepatient movements that occur during a cough; and identify a cough basedon the determination that the accelerometer signal satisfies the set ofcriteria.